1

Electrophysiological evidence   for greater attention to threat when cognitive control resources are depleted

Amanda Holmes 1

Karin Mogg 2

Jan de Fockert 3

Maria Kragh Nielsen 1

Brendan P. Bradley 2

1 Department of Psychology, University of Roehampton, UK

2 Psychology, University of Southampton, UK

3 Department of Psychology, Goldsmiths, University of London, UK

Corresponding author’s address: Amanda Holmes, Department of Psychology, University of

Roehampton, Whitelands College, Holybourne Avenue, London SW15 4JD, England.

Phone 0044 (0)20 8392 3784. Fax 0044 (0)20 8392 3527

Email: a.holmes@roehampton.ac.uk

2

Abstract

This study investigated the time course of attentional bias for threat-related (angry) facial

expressions under conditions of high versus low cognitive (working memory) load. Event-related

potential (ERP) and reaction time (RT) data were recorded while participants viewed pairs of

faces (angry paired with neutral face) displayed for 500 ms and followed by a probe. Participants

were required to respond to the probe while performing a concurrent task of holding in working

memory a sequence of digits that were either in the same order (low memory load) or random

mixed order (high memory load). ERP results revealed that higher working memory load

resulted in enhanced lateralised neural responses to threatening relative to neutral faces,

consistent with greater initial orienting of attention to threatening faces (early N2pc: 180-252

ms) and enhanced maintenance of attention towards threat (late N2pc: 252-320 ms; SPCN: 320-

500 ms). The ERP indices showed significant positive relationships with each other and also

with the behavioural index of attentional bias to threat (reflected by faster RTs to probes

replacing angry than neutral faces at 500 ms), although the latter was not significantly influenced

by memory load. Overall, the findings indicate that depletion of cognitive control resources,

using a working memory manipulation, increases the capacity of task-irrelevant threat cues to

capture and hold attention.

Keywords: ERP; attentional bias; threat; N2pc; SPCN.

Word count: 4,623 (excluding references and figure legends)

3

ACKNOWLEDGEMENTS

This research has been supported by an internal grant from the University of

Roehampton. The authors thank Paul Bretherton for his technical assistance.

4

Several studies indicate that threat stimuli preferentially attract attention (e.g., Calvo et

al., 2007; Holmes et al., 2009; Nummenmaa et al., 2009; Mogg & Bradley, 1999; Öhman et al.,

2001). Evolutionary perspectives suggest that a propensity to orient attention rapidly towards a

possible cue for threat will prepare an individual to deal with potential sources of danger in the

environment (e.g., Davis & Whalen, 2001; Gray, 1982; LeDoux, 1996; McNaughton & Gray,

2000; Öhman et al., 2000). Recent theoretical views propose that threat-related attentional

capture is mediated by specialised emotion processing systems, supported by neural circuitry

centred on the amygdala, which prioritise the attentional selection of stimuli with high

motivational significance (e.g., Öhman & Mineka, 2003; Vuilleumier & Huang, 2009). A ‘biased

competition’ account argues that the amygdala biases the representation of threat in a bottom-up

manner over competing neutral stimuli by means of amygdala feedback to sensory processing

areas of the brain (Pessoa, 2009; Pessoa & Adolphs, 2010; Pourtois, Schettino, & Vuilleumier,

2012; Vuilleumier, 2005).

Biased competition models of selective attention discuss not only mechanisms of bottom-

up biasing, but also mechanisms of top-down frontal control that are engaged to enhance

processing of task-relevant stimuli and minimize interference from task-irrelevant distractor

stimuli (e.g, Desimone & Duncan, 1995; Corbetta et al., 2008). One role of executive control

processes is to maintain templates in working memory (WM) that provide top-down biasing

signals to support task-relevant processes and suppress task-irrelevant processing (i.e., a goal-

directed “attentional set”; De Fockert et al., 2001; Lavie & De Fockert, 2005). The demands on

frontal control processes for the inhibition of task-irrelevant distractors are likely to be

particularly pronounced when the distractors are of a threatening nature, because of the

presumed ‘bottom-up’ enhancement of their signal from emotion processing systems.

5

Following these views, the extent to which attention is allocated to task-irrelevant threat

information depends on competition between biasing effects of bottom-up processes (supporting

threat-related attentional capture) and executive attention control processes (supporting task-

relevant processes). If executive control processes are efficient, attention is more likely to remain

task-focused and less likely to be grabbed by task-irrelevant information. However, if executive

control resources are weak or depleted, task-irrelevant information is less likely to be effectively

inhibited; i.e., under high concurrent cognitive load, threat-related (compared to neutral)

distractors would be more likely to intrude into the focus of attention due to insufficient

executive attention resources to suppress their processing. The primary aim of the present study

is to investigate this directly, by assessing the effect of concurrent WM load on neural and

behavioural measures of the allocation of spatial attention to task-irrelevant threat information.

This research is also guided by Lavie’s (2005; 2010; Lavie et al., 2004) load theory of

attention, which emphasises distinct effects of ‘cognitive control’ load (such as WM load) versus

perceptual load, on distractor processing. That is, distractor interference is increased by high

cognitive control load, but reduced by high perceptual load (see Lavie, 2010, for a review).

Several studies have examined effects of perceptual load on emotion processing (e.g. Bishop et

al., 2007; Fenker et al., 2010; Okon-Singer et al., 2007; Richards et al., 2011), but this work is

outside the scope of the present study, which is concerned instead with the effect of executive

control (‘cognitive’) load on attention to task-irrelevant threat (as discussed earlier). Studies

examining the effect of cognitive control load on emotion processing have produced mixed

findings. For example, some found no effect of manipulating concurrent WM load on

discrimination judgements of emotional versus neutral stimuli (e.g., Phillips et al., 2008; Van

Dillen & Koole, 2009). Others found that high cognitive load (arithmetic task) reduced

6

amygdala response to aversive stimuli that were passively viewed immediately before the

cognitive load (Van Dillen et al., 2009). However, these studies did not directly examine effects

of cognitive load on the allocation of attention to task-irrelevant threat. One exception is

Pecchinenda and Heil (2007; Experiment 3) who reported that the interference effect of

emotional face distractors on valence judgements of emotional word targets (which was used to

index attention) was not significantly affected by concurrent WM load. Similarly, a recent study

by Berggren, Koster, and Derakshan (2012) has also revealed that the capture of attention by

emotional faces in a visual search array was not influenced by a concurrent cognitive load

(counting back in multiples of three). Given that previous relevant evidence is very limited, there

is a clear need to investigate further the effect of manipulating WM load on the allocation of

visuospatial attention to task-irrelevant threat cues.

In the current study we investigated the effect of manipulating top-down executive

control resources on selective attention to task-irrelevant threat whilst participants performed a

visual probe task. In order to vary the resources available for top-down attentional control we

manipulated WM load by requiring participants to remember, across every 2, 3 or 4 visual probe

trials, either a fixed order of digits (low load) or a different order of digits (high load). In the

visual probe task, on each trial, a threat and neutral stimulus were presented simultaneously

(angry and neutral face, side-by-side) and participants required to respond to a target probe that

immediately followed the stimulus pair. Thus, the threat and neutral face in each stimulus pair

competed with each other for attention, with both stimuli being task-irrelevant. The visual probe

task used here provided both neural and behavioural measures of attentional allocation to threat

cues. The behavioural measure of attentional bias to threat is obtained from response times (RTs)

to the probes, with faster RTs to probes replacing threat, relative to neutral, stimuli indicating

7

that attention is drawn preferentially to threat cues. The neural measures of attentional bias to

threat are obtained from lateralised event-related potentials (ERPs) associated with shifts of

attention to the threat versus neutral stimuli in each pair (N2pc, SPCN).

An advantage of the ERP technique is that the time course of attentive processing can be

characterized at a fine temporal resolution. The N2pc reflects rapid shifts in spatial attention to

cue stimuli appearing in the left or right visual field (e.g., Luck & Hillyard, 1994; Woodman &

Luck, 1999). It is typically elicited between 180 and 300 ms post-stimulus onset in the

hemisphere contralateral to the side of the attended stimulus. Previous research has distinguished

between the early and late portions of the N2pc (Eimer & Kiss, 2007; Holmes et al., 2009; Hopf

et al., 2000), with evidence of the early N2pc reflecting the initiation of a shift of attention and

the late N2pc being involved in the filtering of distractors in order to maintain the focus of

attention (Hopf et al., 2000).

A subsequent lateralized ERP component is the SPCN, or sustained posterior

contralateral negativity (~300 to 650 ms; Dell’Acqua et al., 2006; Jolicoeur et al., 2006), which

is also known as the CDA, or contralateral delay activity (Vogel & Machizawa, 2004). The

SPCN is proposed to reflect selection and maintenance of information in visual short-term

memory (Dell’Acqua et al., 2006; Jolicoeur et al., 2006). Maintenance of information in visual

short-term memory has also been related to holding selected stimuli in the focus of attention, i.e.

sustained viusospatial attention (Jonides et al., 2008). Indeed, selective attention and working

memory are increasingly conceptualised as overlapping constructs, as there is growing evidence

that common neural mechanisms support maintenance of attention on both internal and external

stimulus representations (Chun, 2011; Chun et al., 2011; Gazzaley & Nobre, 2012).

8

Previous research into these neural responses to emotional information has shown rapid

initial attentional selection of threat faces, relative to neutral faces, reflected by the early N2pc

(~180-250 ms, e.g., Eimer & Kiss, 2007; Holmes et al., 2009), which was maintained across the

late N2pc (~250-320 ms) and SPCN (~320-500 ms); consistent with a bias in initial orienting and

maintained attention towards threat relative to neutral information over this time-period (Holmes

et al., 2009; see also Feldmann-Wüstefeld et al., 2011).

To recap briefly, allocation of attention to task-irrelevant threat cues is assumed to

depend on the interplay between bottom-up influences (which automatically direct attention to

threat) and top-down influences (which support task-relevant processes and inhibit processing of

task-irrelevant information). If executive control resources are depleted by additional cognitive

demands (e.g. high WM load), attention to task-irrelevant threat should be less effectively

suppressed. Consequently, it is hypothesised that task-irrelevant threat cues will attract greater

attention when there is high concurrent WM load, relative to low concurrent WM load. It is

predicted that this effect will be found for each measure of attentional bias for threat, i.e.

assessed by early N2pc (hypothesis 1), late N2pc (hypothesis 2), SPCN (hypothesis 3), and

manual RTs to probes (hypothesis 4). As each measure is assumed to reflect preferential

allocation of processing resources to threat relative to neutral cues, it is also hypothesised that

these measures will positively correlate with each other (hypothesis 5).

Method

Participants

The participants were 23 healthy volunteers. One participant was excluded because of

excessive eye movement artifacts (>80%), so that 22 participants (3 male and 19 female; 18-41

9

years old; average age: 25.6 years) remained in the sample. All participants had normal or

corrected-to-normal vision and all were right-handed. The experiment was performed in

compliance with relevant institutional guidelines and was approved by the University ethics

committee.

Stimuli and Apparatus

In the visual probe task, face stimuli consisted of pairs of grayscale photographs of 32

different individuals (16 male, 16 female) taken from the NimStim Face Stimulus Set

(Tottenham et al., 2009). Each pair consisted of two pictures of the same individual, with one

photograph portraying an angry expression and the other a neutral expression. An additional set

of neutral face pairs using photographs of four individuals (two male, two female) from the

NimStim Set was used for practice items. Each face was enclosed within a black rectangular

frame measuring 8 cm high x 6.2 cm wide, and the centers of the faces were 5 cm from a white

central fixation cross. The faces within each pair were equated for mean luminance and root

mean square contrast energy using standard routines in Matlab 7. The probe stimuli were white

up- and down-pointing arrows measuring 0.8 cm, and replaced the left or right faces at a position

of 3.75 cm from the central fixation cross.

In the memory task, each digit measured 0.3 cm horizontally and 0.5 cm vertically. Each

memory set (i.e. string of five digits) subtended 2.8 cm horizontally. All stimuli appeared against

a black background. Participants were seated in a dark cabin, and stimuli were presented at a

viewing distance of 70 cm on a 17-in ViewSonic G220f computer screen with a refresh rate of

75 Hz, connected to a Dell Precision Pentium IV computer. Stimulus presentation was controlled

with E-Prime v2.0 (Psychology Software Tools Inc. http://www.pstnet.com/prime). Stimulus

parameters were based on those employed by Holmes et al. (2009) and De Fockert et al. (2001).

10

[** Figure 1 about here **]

Procedure

Each WM load trial contained a memory set (digit string) and memory test, interspersed

by an unpredictable series of visual probe trials, as described below (Figure 1). After a 500 ms

fixation cross, the memory set for that trial was presented for 1500 ms. Under low WM load, the

digit string was always: ‘01234’. Under high WM load, the last four digits were in a new random

order for each trial, e.g. ‘04312’ (‘01234’ and ‘04321’ were excluded from the high WM load

condition). Participants were instructed to remember the order of these digits for the memory

test at the end of the trial. After each memory set, a fixation display was presented for 850 ms,

followed by 2, 3, or 4 visual probe trials. The number of visual probe trials within each memory

task trial was varied to make the onset of the memory probe unpredictable, thus ensuring that the

memory set was actively rehearsed throughout each visual probe trial. After the short series of

visual probe trials, the memory test was presented, which started with a 500 ms fixation cross

followed by a memory probe for 3000 ms. Participants were requested to report the digit that

followed this probe in the memory set by pressing the appropriate key (labelled 1, 2, 3 or 4) on

the numeric keypad on the computer keyboard using their left hand. In order to ensure that all

four responses (including ‘1’ in low WM load trials) were used, a ‘0’ was always the first digit in

each memory set. On each trial, the probe digit was randomly selected, ensuring that it was not

the same as the last digit in the memory set. Immediately following a response, a new WM load

trial was presented. The WM load trial sequence was adapted from De Fockert et al. (2001).

Each visual probe trial started with a central fixation cross (Figure 1). After 500 ms an

angry-neutral face pair was also displayed for a further 500 ms. Immediately after the offset of

11

the face cues and fixation cross, a probe was presented until a response was made or until 6 s had

elapsed. Participants were instructed to press one of two buttons on a purpose-built response box,

using the index finger (upper button) and thumb (lower button) of their right hand, to indicate as

quickly and as accurately as possible the type of probe (i.e., up-pointing arrow [upper key] or

down-pointing arrow [lower key]). Participants were also asked to keep their gaze focused on the

central fixation location throughout the task. There was a variable inter-trial interval (ITI)

ranging from 750 to 1250 ms. This trial sequence was used as previous behavioural and

electrophysiological investigations have shown it to be sensitive to threat-related attentional bias

(e.g., Holmes et al., 2009; Mogg & Bradley, 1998). All visual probe trial types (varying as a

function of position of angry face, probe position, and probe type) were equiprobable across the

experimental trials, and were presented in a new mixed random order for each participant within

each block.

Participants were given two short practice blocks of trials (one high and one low WM

load block); each consisting of 5 memory task trials and 16 visual probe trials. This was followed

by 8 experimental blocks (4 high WM load alternating with 4 low WM load). Each experimental

block consisted of 10 memory task trials and 32 visual probe trials.

EEG data acquisition

EEG was recorded using a Neuroscan 64 channel device (Synamps). Horizontal and

vertical electrooculographs (EOGs) were recorded using four facial bipolar electrodes placed on

the outer canthi of the eyes and in the inferior and superior areas of the left orbit. Scalp EEG was

recorded from 62 Ag/AgCl electrodes mounted in a quickcap (extended 10-20 system). All

electrodes were referenced online to one electrode (vertex) and bandpass filtered at 0.01-100 Hz.

The impedance for electrodes was generally kept below 5 kΩ, and EEG and EOG were sampled

12

online with a digitization rate of 1000 Hz. Following EEG recording, data were down-sampled to

250 Hz to save later computation time, digitally filtered with a low-pass filter at 40 Hz, and all

channels were re-referenced using the average of the mastoids (M1 and M2). EEG and horizontal

EOGs (HEOGs) were epoched offline relative to a 100 ms pre-stimulus baseline, and extending

for 500 ms after stimulus presentation. Trials with lateral eye movements (HEOG exceeding ±

30 μV), as well as trials with vertical eye movements, eye blinks, or other artifacts (a voltage

exceeding ±60 μV at any electrode) measured after target onset were excluded from analysis.

This resulted in the rejection of 36% of trials.

Separate averages were computed for all combinations of WM load (high vs. low), angry

face location (left vs. right), contralaterality (electrodes ipsilateral vs. contralateral to the location

of the emotional face), and component (early N2pc vs. late N2pc vs. SPCN). The ipsilateral

waveform was computed as the average of the left-sided electrodes to the left-sided angry face

and the right-sided electrodes to the right-sided angry face, and the contralateral waveform was

computed as the average of the left-sided electrodes to the right-sided angry face and the right-

sided electrodes to the left-sided angry face. Regional activity was analyzed at lateral posterior

electrodes P7, PO7 (left hemisphere), P8, PO8 (right hemisphere), within post-stimulus time

windows of 180-252 ms (early N2pc), 252-320 ms (late N2pc), and 320-500 ms (SPCN). These

electrode sites and time windows were determined on the basis of inspection of individual

subject waveforms and prior research (e.g., Eimer & Kiss, 2007; Holmes et al., 2009).

Results

Visual probe task: ERP data

13

Figure 2 shows ERPs obtained at electrode sites contralateral to the angry face location

(solid lines) and ipsilateral to the angry face location (dashed lines) for high WM load (top panel)

and low WM load (bottom panel) conditions. In the high load condition, an enhanced negativity

appeared contralateral to angry face cues within the early phase of the N2pc (180-252 ms), and

remained present throughout the late phase of the N2pc (252-320 ms) and the SPCN (320-500

ms). By contrast, there was no evidence of an enhanced negative contralaterality effect under

conditions of low WM load across any of the component time windows. These observations

were confirmed using omnibus analysis of variance (ANOVA) and hypothesis-driven contrasts.

ERP amplitudes were entered into a 2 x 2 x 3 repeated measures ANOVA, with factors of

WM load (high vs. low), Contralaterality (electrodes ipsilateral vs. contralateral to location of

angry face), and Component (early N2pc vs. late N2pc vs. SPCN). There were significant main

effects of Component, F(2,42) = 3.91, p = .05, pη2 = .16, Contralaterality, F(1,21) = 1.84, p

= .002, pη2 = .36, and WM load, F(1,21) = 5.21, p = .03, pη2 = .20, and a significant interaction

between WM load and Contralaterality, F(1,21) = 7.90, p = .01, pη2 = .27. There were no other

significant main effects or interactions. Notably, the WM Load x Contralaterality interaction was

not significantly influenced by Component (F < 1).

To test specific hypotheses and clarify the significant two-way interaction,

contralaterality threat-bias scores (which reflect attentional bias to angry relative to neutral faces)

were calculated by taking the mean amplitude contralateral to angry faces minus the mean

amplitude ipsilateral to angry faces (an angry and neutral face appeared simultaneously in

opposite visual fields on each trial). These bias scores were calculated for each WM load

condition, ERP component and participant. Mean contralaterality threat-bias scores (in μV; SD

in brackets) for the high and low WM load conditions were -0.53 (0.63) and -0.08 (0.53),

14

respectively for early N2pc; -0.60 (0.75) and -0.08 (0.69) for late N2pc; and -0.62 (0.60) and -

0.13 (0.65) for SPCN. Hypothesis-driven paired contrasts indicated that attention bias to angry

faces was significantly increased for each ERP component in the high load condition, relative to

the low load condition (early N2pc: t(21) = 3.27, p = .004, d = .70; late N2pc: t(21) = 2.76, p

= .01, d = .59; SPCN: t(21) = 2.14, p = .04, d = .46). These results support Hypotheses 1 - 3.

[**Figure 2 about here**]

Visual probe task: RT data

RTs were excluded from trials with incorrect responses (1.0% of trials) and outliers (RTs

<200 ms or >1000 ms; 1.7% of trials). RTs were log transformed before analyses to reduce

skewness. Analyses were conducted on transformed data; whereas descriptive statistics are given

for untransformed data for ease of comprehension. Mean RTs in each condition were entered

into a 2 x 2 repeated measure ANOVA, with factors of WM load (high, low) and Congruency

(probe replaces angry face, probe replaces neutral face). There was a significant main effect of

Congruency, F(1,21) = 4.32, p = .05, pη2 = .17, as responses were faster on trials where the probe

and angry face appeared in the same location (congruent trials: M = 557 ms, SD = 72) rather than

opposite location (incongruent trials: M = 561 ms, SD = 71); which is consistent with an

attentional bias towards threat relative to neutral faces. There was no other significant main or

interaction effect. As the congruency effect on RT (indicating attentional bias to threat) was not

significantly influenced by the WM load manipulation (F<1), Hypothesis 4 was not supported.

Memory test

Performance on the memory test indicated that the WM load manipulation was effective.

Participants gave more correct responses to the memory test questions in the low load (M =

96.5%, SD = 3.8) than high load condition (M = 84.9%, SD = 12.2), paired t(21) = 5.15, p < .001,

15

d = 1.54, and their responses were also faster in the low load (M = 1152 ms, SD = 278) than high

load condition (M = 1924 ms, SD = 339), paired t(21) = 13.93, p < .001, d = 3.04.

Correlations between attentional bias measures

Correlations were calculated to test the prediction that the ERP and RT measures of

attentional bias would be positively correlated with each other (Hypothesis 5). The ERP

measures were the contralaterality threat-bias scores for each of the three ERP components

(which reflect enhanced processing of threat relative to neutral faces) described earlier. These

scores were highly inter-correlated (i.e., early N2pc bias score correlated .90 with late N2pc bias

score, and .82 with SPCN bias score; late N2pc bias score correlated .82 with SPCN bias score;

all ps < .01).

The probe RT measure of attention bias was calculated as the difference in mean RT

between incongruent versus congruent trials (this difference corresponds to the ‘Congruency

effect’ in the analysis of RT data described earlier). The RT index of attentional bias correlated

positively with each ERP measure of threat-related bias (i.e., RT bias correlated .43, .45 and .48

with early N2pc, late N2pc and SPCN contralaterality bias scores, respectively; all ps < .05). The

RT bias measure significantly correlated .47, p < .05, with the overall ERP bias index

(contralaterality bias score for threat relative to neutral faces, averaged across the three ERP

components).

Discussion

The aim of the study was to investigate whether threatening faces attract greater attention under

high compared with low concurrent cognitive load. The main results can be summarized as

follows: The ERP results revealed that threat-related attentional bias was significantly greater

16

under conditions of high than low WM load. This enhanced attentional prioritisation of angry

faces was found across three time windows following the onset of the face-pair (corresponding to

early N2pc, late N2pc and SPCN), supporting the first three experimental hypotheses. The probe

RT results showed evidence for an attentional bias towards angry faces; however, this bias was

not modified by WM load, providing no support for our fourth hypothesis. Correlational

evidence showed that the behavioural index of threat-related attentional bias was significantly

associated with each ERP measure of attentional bias, and that the ERP measures were also

significantly inter-correlated, as predicted by our fifth hypothesis.

Only a few studies to date have investigated lateralized ERP correlates of threat-related

attentional bias. As noted earlier, the N2pc has a notable advantage of providing an objective

neural index of early shifts in visuospatial attention, which has high temporal sensitivity. A

consistent finding across previous studies is that attentional shifts towards threatening faces arise

rapidly, with the emergence of an N2pc as early as ~180-250 ms post-stimulus onset (e.g., Eimer

& Kiss, 2007; Fox et al., 2008; Holmes et al., 2009). Various mechanisms have been proposed to

underlie rapid attentional orienting towards sources of threat, including the facilitation of sensory

processing by the amygdala (e.g., Pessoa, 2009; Pessoa & Adolphs, 2010; Pourtois, Schettino, &

Vuilleumier, 2012) and attentional networks in frontoparietal cortex (e.g., Armony & Dolan,

2002). Crucially, the current findings of early N2pc reveal that this initial stage of visuospatial

selection (i.e., 180-252 post-cue onset) is modulated by concurrent demands on processing: when

top-down cognitive control processes were depleted by a concurrent WM task, task-irrelevant

threat was more likely to capture attention. These findings relate to growing evidence that the

attentional processes typically considered to be primarily bottom-up driven (e.g. those involved

in initial attention capture), can be influenced by executive control mechanisms (Kiefer, 2012).

17

Thus, bottom-up influences on attention capture by threat also appear to be context-dependent, as

suggested by the present findings from the early N2pc.

High WM load was also associated with subsequent enhanced processing of threat cues,

as reflected by the late N2pc and SPCN (i.e., 252-320 ms; and 320-500 ms, respectively).

Holmes and colleagues (2009) noted that enhanced processing of angry faces across both N2pc

and SPCN is consistent with proposals that threatening stimuli not only rapidly capture attention

(e.g. Öhman & Mineka, 2003), but also hold it (at least over the relatively short time intervals

assessed here; Fox et al., 2001). From an evolutionary perspective, such holding of attention may

allow novel or potentially significant events to be monitored. Holding attention on threat cues

may relate to maintenance of such information in visual short-term memory, which contributes to

the SPCN (e.g., Dell’Acqua et al., 2006; Jolicoeur et al., 2006; Vogel & Machizawa, 2004). It

should be noted, however, that the SPCN was measured whilst face stimuli were still displayed

on the screen. It is therefore hard to disentangle the extent to which the SPCN findings reflect

maintained processing of threat information in visual short-term memory versus maintained

attention focusing on external threat stimuli. However, as noted earlier, working memory and

selective attention are increasingly conceptualised as closely related, overlapping constructs,

which are supported by common neural mechanisms (Chun et al., 2011; Gazzaley & Nobre,

2012). Thus, the present SPCN results may reflect activation in neural mechanisms associated

with maintaining attention on both internal and external stimulus representations of threat.

The primary results from this study indicate that, when executive control resources are

depleted by additional cognitive demands (i.e., in this case, high WM load), the capture and

holding of attention by task-irrelevant threat cues is enhanced. One explanation for this is that,

under conditions of depleted cognitive control, task-irrelevant threat is less efficiently inhibited

18

and therefore more likely to intrude into the focus of attention. The present findings are novel

because, to our knowledge, no previous study has demonstrated an effect of cognitive (WM) load

on attention allocation to threat. Notably, this effect extends across distinct attentional operations

relating to initial orienting and subsequent maintenance of attention.

Additional notable findings are the significant positive relationships between the

behavioural and ERP measures, which support the view that they reflect common mechanisms

underlying attentional threat-prioritisation. Specifically, greater N2pc and SPCN responses to

threat, relative to neutral, cues, predicted faster RTs to probes which subsequently replaced the

threat cues, which is a widely used behavioural index of attentional bias to threat. Despite these

positive inter-correlations between the RT and ERP measures, only the ERP measures confirmed

the hypothesised effect of cognitive load on increasing attention to task-irrelevant threat. A

recent MEG study also reported that N2pc and RT data showed different effects of processing

demands on attention to threat (Fenker et al., 2010). However, this study examined the effect of

varying perceptual load; which, as noted earlier, is argued to have the opposite effect on

distractor processing than cognitive (WM) load (Lavie, 2010). Results indicated that perceptual

load did not significantly influence N2pc which was elicited by task-irrelevant fearful faces;

whereas RT data showed an attentional bias for fearful faces only when perceptual load was low.

Comparison of results with the present ones is complicated by substantial methodological

differences, such as different types of load manipulation (perceptual versus WM load) and

attentional tasks (visual search versus visual probe). These findings highlight the need to clarify

the effects of cognitive (WM) versus perceptual load manipulations on threat processing, to

assess prioritization of threat processing in the context of Lavie’s (2010) load theory of attention.

Further work is also required in order to determine the extent to which cognitive (WM) load

19

influences the capture of attention by task-irrelevant cues when these cues are more generally

negative or positive in terms of emotional valence, as opposed to being specifically threat-

related.

Regarding the lack of effect of WM load on the behavioural RT index of attentional bias

in this study, it should be noted that the RT index is less direct than the ERP measures and is

obtained after the offset of the threat stimuli (i.e. the RT measure reflects attention allocation to

probes that replace threat cues, rather than attention allocation to the cues per se). These results

highlight the importance of using temporally sensitive measures such as ERPs, as they can

provide a more detailed account of the temporal dynamics of attentive processing than RTs.

In conclusion, to our knowledge, this is the first study to demonstrate a modulatory role

of cognitive control resources on neural processes underlying visuospatial attentional bias to

task-irrelevant threat. Angry face stimuli attracted greater attention when cognitive control

resources were depleted (i.e. under high, relative to low, concurrent WM load). The present

findings indicate the importance of executive processes for the resistance of interference from

distracting threat cues.

20

References

Armony, J.L., & Dolan, R.J. (2002) Modulation of spatial attention by fear-conditioned stimuli:

an event-related fMRI study. Neuropsychologia, 40, 817-26.

Berggren, N., Koster, E.H.W., & Derakshan, N. (2012). The effect of cognitive load in emotional

attention and trait anxiety: An eye movement study. Journal of Cognitive Psychology, 24,

79-91.

Bishop, S.J., Jenkins, R., Lawrence, A.D. (2007) Neural processing of fearful faces: effects of

anxiety are gated by perceptual capacity limitations. Cerebral Cortex, 17, 1595–1603.

Calvo, M.G., Nummenmaa, L., & Hyönä, J. (2007) Emotional and neutral scenes in competition:

orienting, efficiency, and identification. Quarterly Journal of Experimental Psychology,

60, 1585–93.

Corbetta, M., Patel, G., & Shulman, G.L. (2008) The reorienting system of the human brain:

from environment to theory of mind. Neuron, 58, 306–24.

Davis, M., & Whalen, P.J. (2001). The amygdala: vigilance and emotion. Molecular Psychiatry,

6, 13-34.

De Fockert, J.W., Rees, G., Frith, C.D., & Lavie, N. (2001) The role of working memory in

visual selective attention. Science, 291, 1803–6.

Dell'Acqua, R., Sessa, P., Jolicoeur, P., & Robitaille, N. (2006) Spatial attention freezes during

the attention blink. Psychophysiology, 43, 394-400.

Desimone, R., & Duncan, J. (1995) Neural Mechanisms of Selective Visual Attention. Annual

Review of Neuroscience, 18, 193-222.

21

Eimer, M., & Kiss, M. (2007) Attentional capture by task-irrelevant fearful faces is revealed by

the N2pc component. Biological Psychology, 74, 108-112.

Feldmann-Wüstefeld, T., Schmidt-Daffy, M., & Schubö, A. (2011) Neural evidence for the

threat detection advantage: Differential attention allocation to angry and happy faces.

Psychophysiology, 48, 697-707.

Fenker, D.B., Heipertz, D., Boehler, C.N., Schoenfeld, M.A., Noesselt T, Heinze, H-J., et al.

(2010) Mandatory processing of irrelevant fearful face features in visual search. Journal

of Cognitive Neuroscience, 22, 2926–38.

Fox, E., Derakshan, N., & Shoker, L. (2008) Trait anxiety modulates the electrophysiological

indices of rapid spatial orienting towards angry faces. Neuroreport, 19, 259–63.

Fox, E., Russo, R., Bowles, R., & Dutton, K. (2001) Do threatening stimuli draw or hold visual

attention in subclinical anxiety? Journal of Experimental Psychology: General, 130, 681-

700.

Garner, M., Mogg, K., & Bradley, B.P. (2006). Fear-relevant selective associations and social

anxiety: Absence of a positive bias. Behaviour Research and Therapy, 44, 201-217.

Gray, J.A. (1982). The neuropsychology of anxiety: An enquiry into the functions of the septo-

hippocampal system. Oxford: Oxford University Press.

Holmes, A., Kragh Nielsen, M., & Green, S. (2006). Anxiety and sensitivity to eye gaze in

emotional faces. Brain and Cognition, 60, 282-294.

Holmes, A., Bradley, B. P., Kragh Nielsen, M., & Mogg, K. (2009) Attentional selectivity for

emotional faces: Evidence from human electrophysiology. Psychophysiology, 46, 62-68.

22

Hopf, J-M., Luck, S.J., Girelli, M., Hagner, T., Mangun, G.R., Scheich, H., & Heinze, H-J.

(2000). Neural sources of focused attention in visual search. Cerebral Cortex, 10, 1233-

1241.

Jolicoeur, P., Sessa, P., Dell'Acqua, R., & Robitaille, N. (2006). On the control of visual spatial

attention: evidence from human electrophysiology. Psychological Research /

Psychologische Forschung, 70, 414-24.

Jonides, J., Lewis, R.L., Nee, D.E., Lustig, C.A., Berman, M.G., & Moore, K.S. (2008) The

mind and brain of short-term memory. Annual Review of Psychology, 59, 193–224.

Kiefer, M. (2012) Executive control over unconscious cognition: attentional sensitization of

unconscious information processing. Frontiers in Human Neuroscience, 6(61), 1-12.

Lavie, N. (2005) Distracted and confused? Selective attention under load. Trends in Cognitive

Sciences, 9, 75–82.

Lavie, N (2010) Attention, distraction, and cognitive control under load. Current Directions in

Psychological Science 19, 143–48.

Lavie N., & De Fockert J. W. (2005) The role of working memory in attentional capture.

Psychonomic Bulletin & Review, 12, 669–74

Lavie, N., Hirst, A., De Fockert, J. W., & Viding, E. (2004) Load theory of selective attention

and cognitive control. Journal of Experimental Psychology: General, 133, 339-54.

LeDoux, J.E. (1996) The Emotional Brain: The Mysterious Underpinnings of Emotional Life.

New York: Simon & Schuster.

23

Luck, S.J., & Hillyard, S.A. (1994) Spatial filtering during visual search: evidence from human

electrophysiology. Journal of Experimental Psychology. Human Perception and

Performance, 20, 1000-14.

McNaughton, N., & Gray, J.A. (2000) Anxiolytic action on the behavioural inhibition system

implies multiple types of arousal contribute to anxiety. Journal of Affective Disorders,

61, 161-76.

Mogg, K., & Bradley, B.P. (1998) A cognitive-motivational analysis of anxiety. Behaviour

Research and Therapy, 36, 809-48.

Mogg, K., & Bradley, B.P. (1999) Orienting of Attention to Threatening Facial Expressions

Presented under Conditions of Restricted Awareness. Cognition and Emotion, 13, 713-

40.

Nummenmaa, L., Hyönä, J., & Calvo, M.G. (2009) Emotional scene content drives the saccade

generation system reflexively. Journal of Experimental Psychology: Human Perception

and Performance, 35, 305–23.

Okon-Singer, H., Tzelgov, J., & Henik, A. (2007) Distinguishing between automaticity and

attention in the processing of emotionally-significant stimuli. Emotion, 7, 147–57.

Öhman, A., & Mineka, S. (2003) The malicious serpent: Snakes as a prototypical stimulus for an

evolved module for fear. Current Directions in Psychological Science, 12, 5–9.

Öhman, A., Flykt, A., & Esteves, F. (2001) Emotion drives attention: Detecting the snake in the

grass. Journal of Experimental Psychology: General, 130, 466–78.

Öhman, A., Flykt, A., & Lundqvist, D. (2000) Unconscious emotion: Evolutionary perspectives,

psychophysiological data, and neuropsychological mechanisms. In R. Lane & L. Nadel

24

(eds.). The cognitive neuroscience of emotion (p. 296-327). New York: Oxford

University Press.

Pecchinenda, A. & Heil, M. (2007) Role of working memory load on selective attention to

affectively valent information. European Journal of Cognitive Psychology, 19, 898-909.

Pessoa, L. (2009). How do emotion and motivation direct executive function? Trends in

Cognitive Science, 13, 160–66.

Pessoa, L., & Adolphs, R. (2010). Emotion processing and the amygdala: from a ‘low road’ to

‘many roads’ of evaluating biological significance. Nature Reviews Neuroscience, 11,

773-783.

Phillips, L. H., Channon, S., Tunstall, M., Hedenstrom, A., & Lyons, K. (2008) The role of

working memory in decoding emotions. Emotion, 8, 184-91.

Pourtois G., Schettino A., Vuilleumier P. (in press). Brain mechanisms for emotional influences

on perception and attention: what is magic and what is not . Biol. Psychol . [Epub ahead of

print]. doi: 10.1016/j.biopsycho.2012.02.007.

Richards, H.J., Hadwin J.A., Benson, V., Wenger, M.J., & Donnelly, N. (2011) The influence of

anxiety on processing capacity for threat detection. Psychonomic Bulletin Review, 18,

883-9.

Tottenham N., Tanaka J., Leon A.C., McCarry T., Nurse M., & Hare T.A. (2009) The NimStim

set of facial expressions: Judgments from untrained research participants. Psychiatry

Research, 168, 242–49.

Van Dillen, L.F., & Koole, S.L. (2009) How automatic is ’automatic vigilance”? The role of

working memory in attentional interference of negative information. Cognition and

Emotion, 23, 1106-17.

25

Van Dillen, L. F., Heslenfeld, D. J. & Koole, S. L. (2009) Tuning down the emotional brain: an

fMRI study of the effects of cognitive load on the processing of affective images.

Neuroimage, 45, 1212–19.

Vogel, E.K., & Machizawa, M.G. (2004) Neural activity predicts individual differences in visual

working memory capacity. Nature, 428, 748–51.

Vuilleumier P., & Huang Y. M. (2009) Emotional attention: uncovering the mechanisms of

affective biases in perception. Current Directions in Psychological Science, 18, 148–52.

Woodman, G.F., & Luck, S.J. (1999) Electrophysiological measurement of rapid shifts of

attention during visual search. Nature, 400, 867-69.

26

Figure Legends

Figure 1. Example of the sequence of events within a high working memory (WM) load

trial (left panel) and a visual probe trial (right panel). Please note that the stimuli are not to scale.

Figure 2. Grand averaged ERPs for regional analyses of posterior electrode sites (P7, P8,

PO7, PO8) elicited to stimulus pairs containing a neutral and an angry face under high WM load

(top panel) and low WM load (lower panel). ERPs are shown at electrodes contralateral (solid

lines) and ipsilateral (dashed lines) to the angry face.

27

28